We divide the users into three different categories: Lurkers, Engagers, and Contributors. For our analysis, we eliminate lurkers from the data due to the following reasons: (1) They generate no network value on social media, (2) We do not have access to the users' login information which is the time they enter the platform until they leave the application, and (3) The time spent by the user on each content posted on their timeline is unknown such as the duration of the pause before scrolling from the displayed post. We concentrate on engagers to estimate their topic exposure since they are an under-represented group but still leave their footprint on social media. The general framework of the proposed model is shown in Fig.REF . The proposed model
has three major components: (1) Activity vector constructor, (2) Profile information prep module, and (3) Exposure matrix estimator. The model is flexible in the algorithm used to extract the topics.
Based on the application and whether it is important to know the explicit topics or latent topics, we can use methods as simple as a bag of words or TF-IDF or utilize topic modeling approaches such as Latent Dirichlet Allocation (LDA) [1]}, or apply recent methods like BERTopic [2]}.
We describe next in detail each of the model's components and discuss how they are integrated into a framework that predicts the exposure matrix for the engagers.
